Improved lower bounds for self-dual codes over $ \mathbb{F}_{11} $, $ \mathbb{F}_{13} $, $ \mathbb{F}_{17} $, $ \mathbb{F}_{19} $ and $ \mathbb{F}_{23} $
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Bibliographic record
Abstract
<p style='text-indent:20px;'>We construct self-dual codes over <inline-formula><tex-math id="M6">\begin{document}$ \mathbb{F}_{11} $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M7">\begin{document}$ \mathbb{F}_{13} $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M8">\begin{document}$ \mathbb{F}_{17} $\end{document}</tex-math></inline-formula>, <inline-formula><tex-math id="M9">\begin{document}$ \mathbb{F}_{19} $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M10">\begin{document}$ \mathbb{F}_{23} $\end{document}</tex-math></inline-formula> which improve the previously known lower bounds on the largest minimum weights. In particular, the largest possible minimum weight among self-dual <inline-formula><tex-math id="M11">\begin{document}$ [n, n/2] $\end{document}</tex-math></inline-formula> codes over <inline-formula><tex-math id="M12">\begin{document}$ \mathbb{F}_{p} $\end{document}</tex-math></inline-formula> is determined for <inline-formula><tex-math id="M13">\begin{document}$ (p, n) = (19, 24) $\end{document}</tex-math></inline-formula> and <inline-formula><tex-math id="M14">\begin{document}$ (23, 28) $\end{document}</tex-math></inline-formula>.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it